49 lines
1.9 KiB
Markdown
49 lines
1.9 KiB
Markdown
# Data Science Project Checklist
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## Problem Definition & Data Requirements
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- [ ] Business problem is clearly defined
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- [ ] Success metrics are established
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- [ ] Required data sources are identified
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- [ ] Data access methods are established
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- [ ] Privacy and compliance requirements are identified
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- [ ] Project timeline and resources are planned
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## Data Collection & Exploration
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- [ ] Data collection pipeline is established
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- [ ] Data quality assessment is completed
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- [ ] Exploratory data analysis is performed
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- [ ] Data distributions and relationships are understood
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- [ ] Missing data strategy is defined
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- [ ] Outlier handling approach is determined
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## Feature Engineering & Preprocessing
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- [ ] Feature selection/creation strategy is defined
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- [ ] Data transformations are implemented
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- [ ] Feature scaling/normalization is applied where needed
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- [ ] Categorical encoding is implemented appropriately
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- [ ] Data splitting strategy (train/test/validation) is defined
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- [ ] Data preprocessing pipeline is reproducible
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## Model Development
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- [ ] Appropriate algorithms are selected for the problem
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- [ ] Baseline models are established
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- [ ] Hyperparameter tuning strategy is defined
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- [ ] Model evaluation metrics are appropriate for problem
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- [ ] Cross-validation approach is implemented
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- [ ] Model interpretability requirements are met
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## Model Validation & Testing
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- [ ] Models are evaluated on holdout data
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- [ ] Performance meets business requirements
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- [ ] Model generalization is assessed
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- [ ] Model bias and fairness are evaluated
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- [ ] Model limitations are documented
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- [ ] A/B testing plan is defined (if applicable)
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## Deployment & Monitoring
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- [ ] Model deployment approach is defined
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- [ ] Model versioning is implemented
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- [ ] Inference performance is acceptable
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- [ ] Monitoring for model drift is established
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- [ ] Retraining strategy is defined
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- [ ] Feedback loop for model improvement is established |